To detect the existing steganographic algorithms, recent steganalysis methods
usually train a Convolutional Neural Network (CNN) model on the dataset
consisting of corresponding paired cover/stego-images. However, it is
inefficient and impractical for those steganalysis tools to completely retrain
the CNN model to make it effective against both the existing steganographic
algorithms and a new emerging steganographic algorithm. Thus, existing
steganalysis models usually lack dynamic extensibility for new steganographic
algorithms, which limits their application in real-world scenarios. To address
this issue, we propose an accurate parameter importance estimation (APIE)
based-continual learning scheme for steganalysis. In this scheme, when a
steganalysis model is trained on the new image dataset generated by the new
steganographic algorithm, its network parameters are effectively and
efficiently updated with sufficient consideration of their importance evaluated
in the previous training process. This approach can guide the steganalysis
model to learn the patterns of the new steganographic algorithm without
significantly degrading the detectability against the previous steganographic
algorithms. Experimental results demonstrate the proposed scheme has promising
extensibility for new emerging steganographic algorithms.

本文提出了一种基于精确的参数重要度估计（APIE）的连续学习方案，用于检测新兴隐写算法，并应用于实际场景中，通过对之前的训练流程中网络参数的重要性进行评估，在训练新的隐写图像数据集的过程中有效并高效地更新其网络参数，从而使得隐写分析模型能够学习新隐写算法的特征模式，而不会明显降低之前隐写算法的检测性能。实验证明，该方案对新兴隐写算法具有良好的扩展性。